Introduction

This file is a part of the Open Science Framework repository at https://osf.io/djkyf/. A rendered version of this R Markdown file is normally available at this GitHub HTML Preview service page.

Note that in that preview, the ‘tabbed layout’ that makes, for example, userfriendly inspection of the pairwise scatterplots possible, does not work (perhaps GitHub Preview blocks javascript?), so you may want to download the rendered .html in any case.

Analyses

Setup

Loading and preparing data

### Import using haven
dat <-
  haven::read_sav(file.path(dataPath,
                            'database.orig.new labels.sav'));

### ... But convert to a data frame instead of a tibble, because,
### for example, length(unique(dat[, 'group'])) results in 1 instead
### of 2...
dat <- as.data.frame(dat);
dat <- haven::zap_label(dat);
dat <- haven::zap_labels(dat);

### Set group as factor
dat$group <-
  factor(dat$group,
         levels=1:2,
         labels=c("Alternative medicine",
                  "Medicine"));

dat$group_tri <-
  ifelse(dat$group=="Medicine",
         1,
         ifelse(dat$TCM6_simult < 3,
                2,
                ifelse(dat$TCM6_simult > 2,
                       3,
                       NA)));

dat$group_tri <-
  factor(dat$group_tri,
         levels=1:3,
         labels=c("Biomed",
                  "Alternative",
                  "Complementary"));
         
### Extract and store attitude variable names
attitudeVars <-
  grep('ATT1_',
       names(dat),
       value=TRUE);

attitudeVars_2 <-
  grep('ATT2_',
       names(dat),
       value=TRUE);

Construct tree of attitude items

constructTreeYAML <-
  yum::load_yaml_fragments(here::here("methods-construct-tree",
                                      "cam-biomed-attitude-tree-1.dct"));

constructTree <-
  yum::build_tree(constructTreeYAML);

constructTree$Do(function(node) {
  nameFromDataset <-
    grep(node$name,
         names(dat),
         value=TRUE);
  print(nameFromDataset);
  if (length(nameFromDataset) > 0) {
    node$label <- nameFromDataset;
  }},
  filterFun = data.tree::isLeaf);
## [1] "ATT1_4_BodyMirrorToSoul"
## [1] "ATT1_31_IllnessSymbology"
## [1] "ATT1_21_IllnessBecauseOfEmotions"
## [1] "ATT1_33_SymptomsWillDisappear"
## [1] "ATT1_9_MindHasStrongEffect"
## [1] "ATT1_11_BodyRemembers"
## [1] "ATT1_36_UnprocessedTrauma"
## [1] "ATT1_28_ConfrontingEmotionalProb"
## [1] "ATT1_26_HealingResultOfEmoDevelopment"
## [1] "ATT1_18_TreatsOnlySymptoms"
## [1] "ATT1_17_SickByChance"
## [1] "ATT1_20_HealingIsLuck"
## [1] "ATT1_10_AttractPplAndEvents"
## [1] "ATT1_16_EverythingConnected"
## [1] "ATT1_23_NothingByChance"
## [1] "ATT1_2_MustSuffer"
## [1] "ATT1_38_IllnessTeachesUs"
## [1] "ATT1_34_EnergyInEastern"
## [1] "ATT1_27_EnergeticSystemInBody"
## [1] "ATT1_14_Reincarnation"
## [1] "ATT1_6_IllnessIsImbalance"
## [1] "ATT1_40_StrongerComplaintsMeanHealing"
## [1] "ATT1_29_RadiationTherapyHarmful"
## [1] "ATT1_25_ChemotherapyHarmful"
## [1] "ATT1_39_OnlyNatural"
## [1] "ATT1_22_AvoidPharma"
## [1] "ATT1_37_NoBiopsy"
## [1] "ATT1_30_MandatoryVaccines"
## [1] "ATT1_15_TrustAncientRemedies"
## [1] "ATT1_3_TrustInTradRemedy"
## [1] "ATT1_13_ElectronicRadiation"
## [1] "ATT1_7_HealthyDiet"
## [1] "ATT1_1_ExerciseAndDiet"
## [1] "ATT1_12_WeakImmuneSystem"
## [1] "ATT1_24_IllnessBecauseOfGenes"
## [1] "ATT1_32_TrustWesternDocs"
## [1] "ATT1_35_SeriousSymptom"
## [1] "ATT1_5_DependsOnEnvironment"
## [1] "ATT1_19_DoctorMustHealMe"
## [1] "ATT1_8_NeedTestResult"
### Set labels as names
constructTree$Do(function(node) node$name <-
                   node$label);

### Convert to DiagrammeR graph
constructGraph <-
  data.tree::ToDiagrammeRGraph(constructTree);

### Show graph
DiagrammeR::render_graph(constructGraph);
### Export graph
DiagrammeR::export_graph(constructGraph,
                         file_name = here::here("methods-construct-tree",
                                                "cam-biomed-attitude-tree-1.png"));

### Also plot as dendrogram (method not exported by this version of data.tree, oddly)
constructDendro <-
  data.tree:::as.dendrogram.Node(constructTree);

### Get labels in same order
constructTreeLabels <-
  unlist(constructTree$Get('label', filterFun=data.tree::isLeaf));

### For future reference: check
### http://www.sthda.com/english/wiki/beautiful-dendrogram-visualizations-in-r-5-must-known-methods-unsupervised-machine-learning#ggdendro-package-ggplot2-and-dendrogram

ggConstructDendro1 <-
  ggdendro::ggdendrogram(constructDendro,
                         rotate=TRUE,
                         theme_dendro = TRUE) +
  ggplot2::scale_x_continuous(position="top",
                              breaks=seq_along(constructTreeLabels),
                              labels=constructTreeLabels) +
  ggplot2::scale_y_reverse();
## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
print(ggConstructDendro1);

ggsave(filename=here::here("methods-construct-tree",
                           "cam-biomed-dendrogram-1.png"),
       plot=ggConstructDendro1,
       width=12,
       height=19,
       units='cm');

ggConstructDendro2 <-
  constructDendro %>%
    dendextend::set("branches_k_color",
                    value = viridis::viridis(4),
                    k = 4) %>%
    dendextend::as.ggdend() %>%
    ggplot2::ggplot(horiz=TRUE);

print(ggConstructDendro2);
## Warning: Removed 69 rows containing missing values (geom_point).

ggsave(filename=here::here("methods-construct-tree",
                           "cam-biomed-dendrogram-2.png"),
       plot=ggConstructDendro2,
       width=40,
       height=30,
       units='cm');
## Warning: Removed 69 rows containing missing values (geom_point).

Descriptives

Specific descriptives

ufs::cat0("\n\n#### Missing values\n\n");

Missing values

apply(is.na(dat[, attitudeVars]), 2, sum);
           ATT1_1_ExerciseAndDiet 
                                0 
                ATT1_2_MustSuffer 
                                0 
         ATT1_3_TrustInTradRemedy 
                                0 
          ATT1_4_BodyMirrorToSoul 
                                0 
      ATT1_5_DependsOnEnvironment 
                                0 
        ATT1_6_IllnessIsImbalance 
                                0 
               ATT1_7_HealthyDiet 
                                0 
            ATT1_8_NeedTestResult 
                                0 
       ATT1_9_MindHasStrongEffect 
                                0 
      ATT1_10_AttractPplAndEvents 
                                0 
            ATT1_11_BodyRemembers 
                                0 
         ATT1_12_WeakImmuneSystem 
                                0 
      ATT1_13_ElectronicRadiation 
                                0 
            ATT1_14_Reincarnation 
                                0 
     ATT1_15_TrustAncientRemedies 
                                0 
      ATT1_16_EverythingConnected 
                                0 
             ATT1_17_SickByChance 
                                0 
       ATT1_18_TreatsOnlySymptoms 
                                0 
         ATT1_19_DoctorMustHealMe 
                                0 
            ATT1_20_HealingIsLuck 
                                0 
 ATT1_21_IllnessBecauseOfEmotions 
                                0 
              ATT1_22_AvoidPharma 
                                0 
          ATT1_23_NothingByChance 
                                0 
    ATT1_24_IllnessBecauseOfGenes 
                                0 
      ATT1_25_ChemotherapyHarmful 
                                0

ATT1_26_HealingResultOfEmoDevelopment 0 ATT1_27_EnergeticSystemInBody 0 ATT1_28_ConfrontingEmotionalProb 0 ATT1_29_RadiationTherapyHarmful 0 ATT1_30_MandatoryVaccines 0 ATT1_31_IllnessSymbology 0 ATT1_32_TrustWesternDocs 0 ATT1_33_SymptomsWillDisappear 0 ATT1_34_EnergyInEastern 0 ATT1_35_SeriousSymptom 0 ATT1_36_UnprocessedTrauma 0 ATT1_37_NoBiopsy 3 ATT1_38_IllnessTeachesUs 0 ATT1_39_OnlyNatural 0 ATT1_40_StrongerComplaintsMeanHealing 0

ufs::cat0("\n\n#### Attitude\n\n");

Attitude

ufs::meansComparisonDiamondPlot(dat,
                                rev(attitudeVars),
                                compareBy = 'group_tri',
                                comparisonColors = viridis::viridis(3,
                                                                    end=.7),
                                dataAlpha=.25);

Descriptives for all variables

This section uses tabs to show descriptives for all variables.

get_descriptives <- function(data,
                             varName,
                             headerLevel) {
  return(paste0("\n\n",
                ufs::repStr("#", headerLevel),
                " ", varName, "\n\n",
                as.character(pander(userfriendlyscience::descr(data[, varName]))),
                "\n\n"));
}

for (currentVar in names(dat)) {
  cat(get_descriptives(dat,
                       currentVar,
                       headerLevel=4));
}

number

Describing the central tendency:

mean median mode 95% CI mean
81.96 84 NA [74.46; 89.46]

Describing the spread:

var sd iqr se
2262 47.56 83 3.796

Describing the range:

min q1 q3 max
1 39.5 123.5 162

Describing the distribution shape:

skewness kurtosis dip
-0.02789 -1.243 0.01473

Describing the sample size:

total NA. valid
157 0 157

group

  Frequencies Perc.Total Perc.Valid Cumulative
Alternative medicine 95 60.5 60.5 60.5
Medicine 62 39.5 39.5 100
Total valid 157 100 100

birth_yr

Describing the central tendency:

mean median mode 95% CI mean
1970 1973 1974 [1967.55; 1972.66]

Describing the spread:

var sd iqr se
262.4 16.2 28 1.293

Describing the range:

min q1 q3 max
1937 1956 1984 1998

Describing the distribution shape:

skewness kurtosis dip
-0.2984 -1.09 0.02269

Describing the sample size:

total NA. valid
157 0 157

age

Describing the central tendency:

mean median mode 95% CI mean
46.89 44 43 [44.34; 49.45]

Describing the spread:

var sd iqr se
262.4 16.2 28 1.293

Describing the range:

min q1 q3 max
19 33 61 80

Describing the distribution shape:

skewness kurtosis dip
0.2984 -1.09 0.02269

Describing the sample size:

total NA. valid
157 0 157

sex

Describing the central tendency:

mean median mode 95% CI mean
1.72 2 2 [1.65; 1.79]

Describing the spread:

var sd iqr se
0.203 0.4506 1 0.03596

Describing the range:

min q1 q3 max
1 1 NA 2

Describing the distribution shape:

skewness kurtosis dip
-0.988 -1.037 0.1401

Describing the sample size:

total NA. valid
157 0 157

residence

Quitting from lines 237-254 (network-analysis-of-alternative-medicine-attitudes.Rmd) Error in userfriendlyscience::descr(data[, varName]) : The first argument (called ‘x’ in this function, you passed ‘data[, varName]’) is not a numeric vector (it has class ‘character’). Calls: … get_descriptives -> paste0 -> pander ->

religion

Describing the central tendency:

mean median mode 95% CI mean
2.974 1 1 [2.62; 3.33]

Describing the spread:

var sd iqr se
4.901 2.214 4 0.1784

Describing the range:

min q1 q3 max
1 NA 5 7

Describing the distribution shape:

skewness kurtosis dip
0.3445 -1.749 0.1578

Describing the sample size:

total NA. valid
157 3 154

education

Describing the central tendency:

mean median mode 95% CI mean
5.618 6 6 [5.48; 5.76]

Describing the spread:

var sd iqr se
0.7633 0.8737 1 0.06973

Describing the range:

min q1 q3 max
2 4 7 7

Describing the distribution shape:

skewness kurtosis dip
-1.623 2.652 0.06051

Describing the sample size:

total NA. valid
157 0 157

occupation

Describing the central tendency:

mean median mode 95% CI mean
3.777 3 2 [3.38; 4.18]

Describing the spread:

var sd iqr se
6.431 2.536 5 0.2024

Describing the range:

min q1 q3 max
1 2 7 10

Describing the distribution shape:

skewness kurtosis dip
0.7341 -0.9645 0.1178

Describing the sample size:

total NA. valid
157 0 157

TCMclin_7

Describing the central tendency:

mean median mode 95% CI mean
0.1053 0 0 [0.04; 0.17]

Describing the spread:

var sd iqr se
0.09518 0.3085 0 0.03165

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.614 4.936 0.05263

Describing the sample size:

total NA. valid
157 62 95

TCMclin_1

Describing the central tendency:

mean median mode 95% CI mean
0.1053 0 0 [0.04; 0.17]

Describing the spread:

var sd iqr se
0.09518 0.3085 0 0.03165

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.614 4.936 0.05263

Describing the sample size:

total NA. valid
157 62 95

TCMclin_5

Describing the central tendency:

mean median mode 95% CI mean
0.1053 0 0 [0.04; 0.17]

Describing the spread:

var sd iqr se
0.09518 0.3085 0 0.03165

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.614 4.936 0.05263

Describing the sample size:

total NA. valid
157 62 95

TCMclin_8.1

Describing the central tendency:

mean median mode 95% CI mean
0.1263 0 0 [0.06; 0.19]

Describing the spread:

var sd iqr se
0.1115 0.334 0 0.03426

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.286 3.295 0.06316

Describing the sample size:

total NA. valid
157 62 95

TCMclin_8.2

Describing the central tendency:

mean median mode 95% CI mean
0.03158 0 0 [0; 0.07]

Describing the spread:

var sd iqr se
0.03091 0.1758 0 0.01804

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
5.444 28.23 0.01579

Describing the sample size:

total NA. valid
157 62 95

TCMclin_8.3

Describing the central tendency:

mean median mode 95% CI mean
0.1053 0 0 [0.04; 0.17]

Describing the spread:

var sd iqr se
0.09518 0.3085 0 0.03165

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.614 4.936 0.05263

Describing the sample size:

total NA. valid
157 62 95

TCMclin_2.1

Describing the central tendency:

mean median mode 95% CI mean
0.1053 0 0 [0.04; 0.17]

Describing the spread:

var sd iqr se
0.09518 0.3085 0 0.03165

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.614 4.936 0.05263

Describing the sample size:

total NA. valid
157 62 95

TCMclin_2.2

Describing the central tendency:

mean median mode 95% CI mean
0.09474 0 0 [0.03; 0.15]

Describing the spread:

var sd iqr se
0.08667 0.2944 0 0.03021

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.812 6.036 0.04737

Describing the sample size:

total NA. valid
157 62 95

TCMclin_2.3

Describing the central tendency:

mean median mode 95% CI mean
0.02105 0 0 [-0.01; 0.05]

Describing the spread:

var sd iqr se
0.02083 0.1443 0 0.01481

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
6.78 44.91 0.01053

Describing the sample size:

total NA. valid
157 62 95

TCMclin_11.1

Describing the central tendency:

mean median mode 95% CI mean
0.07368 0 0 [0.02; 0.13]

Describing the spread:

var sd iqr se
0.06898 0.2626 0 0.02695

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
3.316 9.19 0.03684

Describing the sample size:

total NA. valid
157 62 95

TCMclin_11.2

Describing the central tendency:

mean median mode 95% CI mean
0.09474 0 0 [0.03; 0.15]

Describing the spread:

var sd iqr se
0.08667 0.2944 0 0.03021

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.812 6.036 0.04737

Describing the sample size:

total NA. valid
157 62 95

TCMclin_12

Describing the central tendency:

mean median mode 95% CI mean
0.03158 0 0 [0; 0.07]

Describing the spread:

var sd iqr se
0.03091 0.1758 0 0.01804

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
5.444 28.23 0.01579

Describing the sample size:

total NA. valid
157 62 95

first_consult

Describing the central tendency:

mean median mode 95% CI mean
1.363e+10 1.369e+10 1.37e+10 [13597369449.37; 13659067628.89]

Describing the spread:

var sd iqr se
2.219e+16 1.49e+08 95904000 15530329

Describing the range:

min q1 q3 max
1.291e+10 1.361e+10 1.371e+10 1.372e+10

Describing the distribution shape:

skewness kurtosis dip
-2.892 9.319 0.03998

Describing the sample size:

total NA. valid
157 65 92

treatments

Describing the central tendency:

mean median mode 95% CI mean
48.19 16 20 [29.72; 66.66]

Describing the spread:

var sd iqr se
7688 87.68 34 9.294

Describing the range:

min q1 q3 max
1 6 45 500

Describing the distribution shape:

skewness kurtosis dip
3.595 15.05 0.04494

Describing the sample size:

total NA. valid
157 68 89

salary

Describing the central tendency:

mean median mode 95% CI mean
3.338 4 4 [3.13; 3.54]

Describing the spread:

var sd iqr se
1.586 1.259 2 0.1035

Describing the range:

min q1 q3 max
1 2 5 5

Describing the distribution shape:

skewness kurtosis dip
-0.3728 -0.914 0.1014

Describing the sample size:

total NA. valid
157 9 148

family_status

Describing the central tendency:

mean median mode 95% CI mean
2.609 2 1 [2.36; 2.86]

Describing the spread:

var sd iqr se
2.498 1.58 3 0.1265

Describing the range:

min q1 q3 max
1 1 4 5

Describing the distribution shape:

skewness kurtosis dip
0.2789 -1.597 0.1306

Describing the sample size:

total NA. valid
157 1 156

children

Describing the central tendency:

mean median mode 95% CI mean
0.8471 1 0 [0.69; 1]

Describing the spread:

var sd iqr se
0.9508 0.9751 2 0.07782

Describing the range:

min q1 q3 max
0 0 2 5

Describing the distribution shape:

skewness kurtosis dip
0.9845 0.8398 0.121

Describing the sample size:

total NA. valid
157 0 157

pres_compl

Quitting from lines 237-254 (network-analysis-of-alternative-medicine-attitudes.Rmd) Error in userfriendlyscience::descr(data[, varName]) : The first argument (called ‘x’ in this function, you passed ‘data[, varName]’) is not a numeric vector (it has class ‘character’). Calls: … get_descriptives -> paste0 -> pander ->

GP_5

Describing the central tendency:

mean median mode 95% CI mean
0.1613 0 0 [0.07; 0.26]

Describing the spread:

var sd iqr se
0.1375 0.3708 0 0.04709

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.888 1.615 0.08065

Describing the sample size:

total NA. valid
157 95 62

GP_9

Describing the central tendency:

mean median mode 95% CI mean
0.1613 0 0 [0.07; 0.26]

Describing the spread:

var sd iqr se
0.1375 0.3708 0 0.04709

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.888 1.615 0.08065

Describing the sample size:

total NA. valid
157 95 62

GP_1

Describing the central tendency:

mean median mode 95% CI mean
0.1774 0 0 [0.08; 0.28]

Describing the spread:

var sd iqr se
0.1483 0.3851 0 0.04891

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.731 1.028 0.08871

Describing the sample size:

total NA. valid
157 95 62

GP_11

Describing the central tendency:

mean median mode 95% CI mean
0.1613 0 0 [0.07; 0.26]

Describing the spread:

var sd iqr se
0.1375 0.3708 0 0.04709

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.888 1.615 0.08065

Describing the sample size:

total NA. valid
157 95 62

GP_13

Describing the central tendency:

mean median mode 95% CI mean
0.1774 0 0 [0.08; 0.28]

Describing the spread:

var sd iqr se
0.1483 0.3851 0 0.04891

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.731 1.028 0.08871

Describing the sample size:

total NA. valid
157 95 62

GP_2

Describing the central tendency:

mean median mode 95% CI mean
0.1613 0 0 [0.07; 0.26]

Describing the spread:

var sd iqr se
0.1375 0.3708 0 0.04709

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.888 1.615 0.08065

Describing the sample size:

total NA. valid
157 95 62 
## Warning: Computation failed in `stat_bin()`:
## `binwidth` must be positive
## Warning: Removed 1 rows containing missing values (geom_abline).
## Warning: Computation failed in `stat_bin()`:
## `binwidth` must be positive
## Warning: Removed 1 rows containing missing values (geom_abline).

GPwhy_1

Describing the central tendency:

mean median mode 95% CI mean
0 0 0 [0; 0]

Describing the spread:

var sd iqr se
0 0 0 0

Describing the range:

min q1 q3 max
0 NA NA 0

Describing the distribution shape:

skewness kurtosis dip
NA NA 0.008065

Describing the sample size:

total NA. valid
157 95 62

GPwhy_2

Describing the central tendency:

mean median mode 95% CI mean
0.129 0 0 [0.04; 0.21]

Describing the spread:

var sd iqr se
0.1142 0.338 0 0.04292

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.268 3.25 0.06452

Describing the sample size:

total NA. valid
157 95 62

GPwhy_3

Describing the central tendency:

mean median mode 95% CI mean
0.01613 0 0 [-0.02; 0.05]

Describing the spread:

var sd iqr se
0.01613 0.127 0 0.01613

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
7.874 62 0.008065

Describing the sample size:

total NA. valid
157 95 62

GPwhy_4

Describing the central tendency:

mean median mode 95% CI mean
0.2903 0 0 [0.17; 0.41]

Describing the spread:

var sd iqr se
0.2094 0.4576 1 0.05812

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
0.9469 -1.141 0.1452

Describing the sample size:

total NA. valid
157 95 62 
## Warning: Computation failed in `stat_bin()`:
## `binwidth` must be positive

## Warning: Removed 1 rows containing missing values (geom_abline).
## Warning: Computation failed in `stat_bin()`:
## `binwidth` must be positive
## Warning: Removed 1 rows containing missing values (geom_abline).

GPwhy_5

Describing the central tendency:

mean median mode 95% CI mean
0 0 0 [0; 0]

Describing the spread:

var sd iqr se
0 0 0 0

Describing the range:

min q1 q3 max
0 NA NA 0

Describing the distribution shape:

skewness kurtosis dip
NA NA 0.008065

Describing the sample size:

total NA. valid
157 95 62

GPwhy_6

Describing the central tendency:

mean median mode 95% CI mean
0.2097 0 0 [0.11; 0.31]

Describing the spread:

var sd iqr se
0.1684 0.4104 0 0.05212

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.462 0.1409 0.1048

Describing the sample size:

total NA. valid
157 95 62

GPwhy_7

Describing the central tendency:

mean median mode 95% CI mean
0.3871 0 0 [0.26; 0.51]

Describing the spread:

var sd iqr se
0.2411 0.4911 1 0.06236

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
0.4752 -1.834 0.1935

Describing the sample size:

total NA. valid
157 95 62

GPwhy_8

Describing the central tendency:

mean median mode 95% CI mean
0.09677 0 0 [0.02; 0.17]

Describing the spread:

var sd iqr se
0.08884 0.2981 0 0.03785

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.796 6.01 0.04839

Describing the sample size:

total NA. valid
157 95 62

GPwhy_9

Describing the central tendency:

mean median mode 95% CI mean
0.129 0 0 [0.04; 0.21]

Describing the spread:

var sd iqr se
0.1142 0.338 0 0.04292

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.268 3.25 0.06452

Describing the sample size:

total NA. valid
157 95 62

GPwhy_10

Describing the central tendency:

mean median mode 95% CI mean
0.08065 0 0 [0.01; 0.15]

Describing the spread:

var sd iqr se
0.07536 0.2745 0 0.03486

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
3.157 8.232 0.04032

Describing the sample size:

total NA. valid
157 95 62

GPwhy_11

Describing the central tendency:

mean median mode 95% CI mean
0.2419 0 0 [0.13; 0.35]

Describing the spread:

var sd iqr se
0.1864 0.4318 0 0.05483

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.235 -0.491 0.121

Describing the sample size:

total NA. valid
157 95 62

GPwhy_other

Quitting from lines 237-254 (network-analysis-of-alternative-medicine-attitudes.Rmd) Error in userfriendlyscience::descr(data[, varName]) : The first argument (called ‘x’ in this function, you passed ‘data[, varName]’) is not a numeric vector (it has class ‘character’). Calls: … get_descriptives -> paste0 -> pander ->

TCM1_ethnicity

Describing the central tendency:

mean median mode 95% CI mean
1.179 1 1 [1.1; 1.26]

Describing the spread:

var sd iqr se
0.1485 0.3853 0 0.03954

Describing the range:

min q1 q3 max
1 NA 2 2

Describing the distribution shape:

skewness kurtosis dip
1.702 0.9162 0.08947

Describing the sample size:

total NA. valid
157 62 95

TCM2_contact

Describing the central tendency:

mean median mode 95% CI mean
1.968 2 2 [1.8; 2.13]

Describing the spread:

var sd iqr se
0.6549 0.8092 0 0.08347

Describing the range:

min q1 q3 max
0 1 4 5

Describing the distribution shape:

skewness kurtosis dip
1.054 5.114 0.05319

Describing the sample size:

total NA. valid
157 63 94

TCM2_other

Quitting from lines 237-254 (network-analysis-of-alternative-medicine-attitudes.Rmd) Error in userfriendlyscience::descr(data[, varName]) : The first argument (called ‘x’ in this function, you passed ‘data[, varName]’) is not a numeric vector (it has class ‘character’). Calls: … get_descriptives -> paste0 -> pander ->

TCM3.1_why

Describing the central tendency:

mean median mode 95% CI mean
0.01053 0 0 [-0.01; 0.03]

Describing the spread:

var sd iqr se
0.01053 0.1026 0 0.01053

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
9.747 95 0.005263

Describing the sample size:

total NA. valid
157 62 95

TCM3.2

Describing the central tendency:

mean median mode 95% CI mean
0.4526 0 0 [0.35; 0.55]

Describing the spread:

var sd iqr se
0.2504 0.5004 1 0.05134

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
0.1934 -2.005 0.2263

Describing the sample size:

total NA. valid
157 62 95

TCM3.3

Describing the central tendency:

mean median mode 95% CI mean
0.04211 0 0 [0; 0.08]

Describing the spread:

var sd iqr se
0.04076 0.2019 0 0.02071

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
4.634 19.89 0.02105

Describing the sample size:

total NA. valid
157 62 95

TCM3.4

Describing the central tendency:

mean median mode 95% CI mean
0.4 0 0 [0.3; 0.5]

Describing the spread:

var sd iqr se
0.2426 0.4925 1 0.05053

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
0.4148 -1.868 0.2

Describing the sample size:

total NA. valid
157 62 95

TCM3.5

Describing the central tendency:

mean median mode 95% CI mean
0.6105 1 1 [0.51; 0.71]

Describing the spread:

var sd iqr se
0.2403 0.4902 1 0.0503

Describing the range:

min q1 q3 max
0 0 NA 1

Describing the distribution shape:

skewness kurtosis dip
-0.4606 -1.827 0.1947

Describing the sample size:

total NA. valid
157 62 95

TCM3.6

Describing the central tendency:

mean median mode 95% CI mean
0.2 0 0 [0.12; 0.28]

Describing the spread:

var sd iqr se
0.1617 0.4021 0 0.04126

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.524 0.3296 0.1

Describing the sample size:

total NA. valid
157 62 95

TCM3.7

Describing the central tendency:

mean median mode 95% CI mean
0.5368 0 0 [0.37; 0.71]

Describing the spread:

var sd iqr se
0.6981 0.8355 1 0.08572

Describing the range:

min q1 q3 max
0 NA 1 7

Describing the distribution shape:

skewness kurtosis dip
4.967 37.65 0.2316

Describing the sample size:

total NA. valid
157 62 95

TCM3.8

Quitting from lines 237-254 (network-analysis-of-alternative-medicine-attitudes.Rmd) Error in userfriendlyscience::descr(data[, varName]) : The first argument (called ‘x’ in this function, you passed ‘data[, varName]’) is not a numeric vector (it has class ‘character’). Calls: … get_descriptives -> paste0 -> pander ->

TCM4_illness

Describing the central tendency:

mean median mode 95% CI mean
1.579 1 1 [1.44; 1.72]

Describing the spread:

var sd iqr se
0.4591 0.6776 1 0.06952

Describing the range:

min q1 q3 max
1 NA 2 3

Describing the distribution shape:

skewness kurtosis dip
0.7547 -0.5432 0.1842

Describing the sample size:

total NA. valid
157 62 95

TCM5.1

Describing the central tendency:

mean median mode 95% CI mean
0.08421 0 0 [0.03; 0.14]

Describing the spread:

var sd iqr se
0.07794 0.2792 0 0.02864

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
3.043 7.414 0.04211

Describing the sample size:

total NA. valid
157 62 95

TCM5.2

Describing the central tendency:

mean median mode 95% CI mean
0.1895 0 0 [0.11; 0.27]

Describing the spread:

var sd iqr se
0.1552 0.394 0 0.04042

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.61 0.6054 0.09474

Describing the sample size:

total NA. valid
157 62 95

TCM5.3

Describing the central tendency:

mean median mode 95% CI mean
0.04211 0 0 [0; 0.08]

Describing the spread:

var sd iqr se
0.04076 0.2019 0 0.02071

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
4.634 19.89 0.02105

Describing the sample size:

total NA. valid
157 62 95

TCM5.4

Describing the central tendency:

mean median mode 95% CI mean
0.4632 0 0 [0.34; 0.59]

Describing the spread:

var sd iqr se
0.3789 0.6156 1 0.06316

Describing the range:

min q1 q3 max
0 NA 1 4

Describing the distribution shape:

skewness kurtosis dip
2.099 9.717 0.2105

Describing the sample size:

total NA. valid
157 62 95

TCM5.5

Describing the central tendency:

mean median mode 95% CI mean
0.02105 0 0 [-0.01; 0.05]

Describing the spread:

var sd iqr se
0.02083 0.1443 0 0.01481

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
6.78 44.91 0.01053

Describing the sample size:

total NA. valid
157 62 95

TCM5.6

Describing the central tendency:

mean median mode 95% CI mean
0.3263 0 0 [0.23; 0.42]

Describing the spread:

var sd iqr se
0.2222 0.4714 1 0.04836

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
0.7528 -1.465 0.1632

Describing the sample size:

total NA. valid
157 62 95

TCM5.7

Describing the central tendency:

mean median mode 95% CI mean
0.1368 0 0 [0.07; 0.21]

Describing the spread:

var sd iqr se
0.1194 0.3455 0 0.03545

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.147 2.667 0.06842

Describing the sample size:

total NA. valid
157 62 95

TCM5.8

Describing the central tendency:

mean median mode 95% CI mean
0.2 0 0 [0.12; 0.28]

Describing the spread:

var sd iqr se
0.1617 0.4021 0 0.04126

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.524 0.3296 0.1

Describing the sample size:

total NA. valid
157 62 95

TCM5.9

Describing the central tendency:

mean median mode 95% CI mean
0.1368 0 0 [0.07; 0.21]

Describing the spread:

var sd iqr se
0.1194 0.3455 0 0.03545

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.147 2.667 0.06842

Describing the sample size:

total NA. valid
157 62 95

TCM5.10

Describing the central tendency:

mean median mode 95% CI mean
0.02105 0 0 [-0.01; 0.05]

Describing the spread:

var sd iqr se
0.02083 0.1443 0 0.01481

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
6.78 44.91 0.01053

Describing the sample size:

total NA. valid
157 62 95

TCM5.11

Describing the central tendency:

mean median mode 95% CI mean
0.1053 0 0 [0.04; 0.17]

Describing the spread:

var sd iqr se
0.09518 0.3085 0 0.03165

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.614 4.936 0.05263

Describing the sample size:

total NA. valid
157 62 95

TCM5.12

Describing the central tendency:

mean median mode 95% CI mean
0.1053 0 0 [0.04; 0.17]

Describing the spread:

var sd iqr se
0.09518 0.3085 0 0.03165

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.614 4.936 0.05263

Describing the sample size:

total NA. valid
157 62 95

TCM5.13

Describing the central tendency:

mean median mode 95% CI mean
0.2632 0 0 [0.17; 0.35]

Describing the spread:

var sd iqr se
0.196 0.4427 1 0.04542

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.093 -0.823 0.1316

Describing the sample size:

total NA. valid
157 62 95

TCM5.14

Describing the central tendency:

mean median mode 95% CI mean
0.09474 0 0 [0.03; 0.15]

Describing the spread:

var sd iqr se
0.08667 0.2944 0 0.03021

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.812 6.036 0.04737

Describing the sample size:

total NA. valid
157 62 95

TCM5.15

Describing the central tendency:

mean median mode 95% CI mean
0.1579 0 0 [0.08; 0.23]

Describing the spread:

var sd iqr se
0.1344 0.3666 0 0.03761

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.907 1.67 0.07895

Describing the sample size:

total NA. valid
157 62 95

TCM5.16

Describing the central tendency:

mean median mode 95% CI mean
0.06316 0 0 [0.01; 0.11]

Describing the spread:

var sd iqr se
0.0598 0.2445 0 0.02509

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
3.65 11.56 0.03158

Describing the sample size:

total NA. valid
157 62 95

TCM5.17

Describing the central tendency:

mean median mode 95% CI mean
0.01053 0 0 [-0.01; 0.03]

Describing the spread:

var sd iqr se
0.01053 0.1026 0 0.01053

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
9.747 95 0.005263

Describing the sample size:

total NA. valid
157 62 95

TCM5.18

Describing the central tendency:

mean median mode 95% CI mean
0.07368 0 0 [0.02; 0.13]

Describing the spread:

var sd iqr se
0.06898 0.2626 0 0.02695

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
3.316 9.19 0.03684

Describing the sample size:

total NA. valid
157 62 95

TCM5.19

Describing the central tendency:

mean median mode 95% CI mean
0.3263 0 0 [0.23; 0.42]

Describing the spread:

var sd iqr se
0.2222 0.4714 1 0.04836

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
0.7528 -1.465 0.1632

Describing the sample size:

total NA. valid
157 62 95

TCM5.20

Quitting from lines 237-254 (network-analysis-of-alternative-medicine-attitudes.Rmd) Error in userfriendlyscience::descr(data[, varName]) : The first argument (called ‘x’ in this function, you passed ‘data[, varName]’) is not a numeric vector (it has class ‘character’). Calls: … get_descriptives -> paste0 -> pander ->

TCM6_simult

Describing the central tendency:

mean median mode 95% CI mean
2.242 3 3 [2.02; 2.46]

Describing the spread:

var sd iqr se
1.185 1.089 2 0.1117

Describing the range:

min q1 q3 max
1 1 4 4

Describing the distribution shape:

skewness kurtosis dip
0.00604 -1.51 0.1947

Describing the sample size:

total NA. valid
157 62 95

TCM7.1

Describing the central tendency:

mean median mode 95% CI mean
0.09574 0 0 [0.04; 0.16]

Describing the spread:

var sd iqr se
0.08751 0.2958 0 0.03051

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.793 5.924 0.04787

Describing the sample size:

total NA. valid
157 63 94

TCM7.2

Describing the central tendency:

mean median mode 95% CI mean
0.03191 0 0 [0; 0.07]

Describing the spread:

var sd iqr se
0.03123 0.1767 0 0.01823

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
5.413 27.89 0.01596

Describing the sample size:

total NA. valid
157 63 94

TCM7.3

Describing the central tendency:

mean median mode 95% CI mean
0.1596 0 0 [0.08; 0.23]

Describing the spread:

var sd iqr se
0.1356 0.3682 0 0.03797

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.889 1.604 0.07979

Describing the sample size:

total NA. valid
157 63 94

TCM7.4

Describing the central tendency:

mean median mode 95% CI mean
0.06383 0 0 [0.01; 0.11]

Describing the spread:

var sd iqr se
0.0604 0.2458 0 0.02535

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
3.627 11.4 0.03191

Describing the sample size:

total NA. valid
157 63 94

TCM7.5

Describing the central tendency:

mean median mode 95% CI mean
0.1809 0 0 [0.1; 0.26]

Describing the spread:

var sd iqr se
0.1497 0.387 0 0.03991

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.685 0.8583 0.09043

Describing the sample size:

total NA. valid
157 63 94

TCM7.6

Describing the central tendency:

mean median mode 95% CI mean
0.08511 0 0 [0.03; 0.14]

Describing the spread:

var sd iqr se
0.0787 0.2805 0 0.02894

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
3.022 7.288 0.04255

Describing the sample size:

total NA. valid
157 63 94

TCM7.7

Describing the central tendency:

mean median mode 95% CI mean
0.2979 0 0 [0.2; 0.39]

Describing the spread:

var sd iqr se
0.2114 0.4598 1 0.04742

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
0.8984 -1.219 0.1489

Describing the sample size:

total NA. valid
157 63 94

TCM7.8

Describing the central tendency:

mean median mode 95% CI mean
0.2021 0 0 [0.12; 0.28]

Describing the spread:

var sd iqr se
0.163 0.4037 0 0.04164

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.508 0.2785 0.1011

Describing the sample size:

total NA. valid
157 63 94

TCM7.9

Describing the central tendency:

mean median mode 95% CI mean
0.1277 0 0 [0.06; 0.2]

Describing the spread:

var sd iqr se
0.1126 0.3355 0 0.0346

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.268 3.211 0.06383

Describing the sample size:

total NA. valid
157 63 94

TCM7.10

Quitting from lines 237-254 (network-analysis-of-alternative-medicine-attitudes.Rmd) Error in userfriendlyscience::descr(data[, varName]) : The first argument (called ‘x’ in this function, you passed ‘data[, varName]’) is not a numeric vector (it has class ‘character’). Calls: … get_descriptives -> paste0 -> pander ->

TCM8_doc_discl

Describing the central tendency:

mean median mode 95% CI mean
2.663 2 2 [2.39; 2.93]

Describing the spread:

var sd iqr se
1.758 1.326 2 0.136

Describing the range:

min q1 q3 max
0 1 4 5

Describing the distribution shape:

skewness kurtosis dip
0.1986 -1.161 0.1316

Describing the sample size:

total NA. valid
157 62 95

TCM8_depends

Quitting from lines 237-254 (network-analysis-of-alternative-medicine-attitudes.Rmd) Error in userfriendlyscience::descr(data[, varName]) : The first argument (called ‘x’ in this function, you passed ‘data[, varName]’) is not a numeric vector (it has class ‘character’). Calls: … get_descriptives -> paste0 -> pander ->

TCM9.1_nondiscl

Describing the central tendency:

mean median mode 95% CI mean
0.2553 0 0 [0.17; 0.35]

Describing the spread:

var sd iqr se
0.1922 0.4384 1 0.04522

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.141 -0.7148 0.1277

Describing the sample size:

total NA. valid
157 63 94

TCM9.2

Describing the central tendency:

mean median mode 95% CI mean
0.1383 0 0 [0.07; 0.21]

Describing the spread:

var sd iqr se
0.1205 0.3471 0 0.0358

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.13 2.59 0.06915

Describing the sample size:

total NA. valid
157 63 94

TCM9.3

Describing the central tendency:

mean median mode 95% CI mean
0.03191 0 0 [0; 0.07]

Describing the spread:

var sd iqr se
0.03123 0.1767 0 0.01823

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
5.413 27.89 0.01596

Describing the sample size:

total NA. valid
157 63 94

TCM9.4

Describing the central tendency:

mean median mode 95% CI mean
0.01064 0 0 [-0.01; 0.03]

Describing the spread:

var sd iqr se
0.01064 0.1031 0 0.01064

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
9.695 94 0.005319

Describing the sample size:

total NA. valid
157 63 94

TCM9.5

Describing the central tendency:

mean median mode 95% CI mean
0.1489 0 0 [0.08; 0.22]

Describing the spread:

var sd iqr se
0.1281 0.3579 0 0.03692

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.004 2.06 0.07447

Describing the sample size:

total NA. valid
157 63 94

TCM10_CAM

Describing the central tendency:

mean median mode 95% CI mean
1.447 1 1 [1.32; 1.57]

Describing the spread:

var sd iqr se
0.3789 0.6155 1 0.06349

Describing the range:

min q1 q3 max
0 0 2 3

Describing the distribution shape:

skewness kurtosis dip
0.772 -0.001887 0.1755

Describing the sample size:

total NA. valid
157 63 94

TCM11_immune

Describing the central tendency:

mean median mode 95% CI mean
0.1158 0 0 [0.05; 0.18]

Describing the spread:

var sd iqr se
0.1035 0.3217 0 0.033

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.44 4.039 0.05789

Describing the sample size:

total NA. valid
157 62 95

TCM11_nerves

Describing the central tendency:

mean median mode 95% CI mean
0.2105 0 0 [0.13; 0.29]

Describing the spread:

var sd iqr se
0.168 0.4098 0 0.04205

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
1.443 0.0835 0.1053

Describing the sample size:

total NA. valid
157 62 95

TCM11_energy

Describing the central tendency:

mean median mode 95% CI mean
0.8 1 1 [0.72; 0.88]

Describing the spread:

var sd iqr se
0.1617 0.4021 0 0.04126

Describing the range:

min q1 q3 max
0 0 NA 1

Describing the distribution shape:

skewness kurtosis dip
-1.524 0.3296 0.1

Describing the sample size:

total NA. valid
157 62 95

TCM11_soul

Describing the central tendency:

mean median mode 95% CI mean
0.1474 0 0 [0.07; 0.22]

Describing the spread:

var sd iqr se
0.127 0.3564 0 0.03656

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
2.022 2.132 0.07368

Describing the sample size:

total NA. valid
157 62 95

TCM11_other

Quitting from lines 237-254 (network-analysis-of-alternative-medicine-attitudes.Rmd) Error in userfriendlyscience::descr(data[, varName]) : The first argument (called ‘x’ in this function, you passed ‘data[, varName]’) is not a numeric vector (it has class ‘character’). Calls: … get_descriptives -> paste0 -> pander ->

TCM12_efficacy

Describing the central tendency:

mean median mode 95% CI mean
3.095 3 3 [3.01; 3.18]

Describing the spread:

var sd iqr se
0.1931 0.4394 0 0.04508

Describing the range:

min q1 q3 max
2 2 4 4

Describing the distribution shape:

skewness kurtosis dip
0.4819 1.949 0.07368

Describing the sample size:

total NA. valid
157 62 95

ATT1_1_ExerciseAndDiet

Describing the central tendency:

mean median mode 95% CI mean
4.777 5 5 [4.59; 4.96]

Describing the spread:

var sd iqr se
1.405 1.185 2 0.0946

Describing the range:

min q1 q3 max
1 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-1.359 2.064 0.1433

Describing the sample size:

total NA. valid
157 0 157

ATT1_2_MustSuffer

Describing the central tendency:

mean median mode 95% CI mean
3.191 3 2 [2.96; 3.42]

Describing the spread:

var sd iqr se
2.181 1.477 2 0.1179

Describing the range:

min q1 q3 max
1 2 5 6

Describing the distribution shape:

skewness kurtosis dip
0.1851 -0.9792 0.1019

Describing the sample size:

total NA. valid
157 0 157

ATT1_3_TrustInTradRemedy

Describing the central tendency:

mean median mode 95% CI mean
3.936 4 4 [3.71; 4.17]

Describing the spread:

var sd iqr se
2.137 1.462 2 0.1167

Describing the range:

min q1 q3 max
1 2 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.3871 -0.6568 0.1083

Describing the sample size:

total NA. valid
157 0 157

ATT1_4_BodyMirrorToSoul

Describing the central tendency:

mean median mode 95% CI mean
4.783 5 5 [4.58; 4.98]

Describing the spread:

var sd iqr se
1.607 1.268 2 0.1012

Describing the range:

min q1 q3 max
1 3.5 6 6

Describing the distribution shape:

skewness kurtosis dip
-1.191 1.049 0.1688

Describing the sample size:

total NA. valid
157 0 157

ATT1_5_DependsOnEnvironment

Describing the central tendency:

mean median mode 95% CI mean
2.822 3 2 [2.62; 3.02]

Describing the spread:

var sd iqr se
1.635 1.279 2 0.102

Describing the range:

min q1 q3 max
1 2 4.5 6

Describing the distribution shape:

skewness kurtosis dip
0.4701 -0.4979 0.1401

Describing the sample size:

total NA. valid
157 0 157

ATT1_6_IllnessIsImbalance

Describing the central tendency:

mean median mode 95% CI mean
5.115 5 6 [4.96; 5.27]

Describing the spread:

var sd iqr se
0.9483 0.9738 1 0.07772

Describing the range:

min q1 q3 max
1 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-1.119 1.44 0.1624

Describing the sample size:

total NA. valid
157 0 157

ATT1_7_HealthyDiet

Describing the central tendency:

mean median mode 95% CI mean
5.146 5 5 [5.02; 5.28]

Describing the spread:

var sd iqr se
0.6771 0.8229 1 0.06567

Describing the range:

min q1 q3 max
2 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.8381 0.6823 0.1879

Describing the sample size:

total NA. valid
157 0 157

ATT1_8_NeedTestResult

Describing the central tendency:

mean median mode 95% CI mean
4.369 5 5 [4.15; 4.59]

Describing the spread:

var sd iqr se
1.952 1.397 1 0.1115

Describing the range:

min q1 q3 max
1 3 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.7556 -0.2016 0.1146

Describing the sample size:

total NA. valid
157 0 157

ATT1_9_MindHasStrongEffect

Describing the central tendency:

mean median mode 95% CI mean
4.892 5 5 [4.72; 5.07]

Describing the spread:

var sd iqr se
1.213 1.101 2 0.08788

Describing the range:

min q1 q3 max
1 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-1.067 0.9044 0.1688

Describing the sample size:

total NA. valid
157 0 157

ATT1_10_AttractPplAndEvents

Describing the central tendency:

mean median mode 95% CI mean
4.554 5 5 [4.35; 4.76]

Describing the spread:

var sd iqr se
1.71 1.308 2 0.1044

Describing the range:

min q1 q3 max
1 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.9558 0.491 0.1306

Describing the sample size:

total NA. valid
157 0 157

ATT1_11_BodyRemembers

Describing the central tendency:

mean median mode 95% CI mean
4.65 5 5 [4.47; 4.83]

Describing the spread:

var sd iqr se
1.344 1.159 2 0.09254

Describing the range:

min q1 q3 max
1 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.7074 0.1781 0.1369

Describing the sample size:

total NA. valid
157 0 157

ATT1_12_WeakImmuneSystem

Describing the central tendency:

mean median mode 95% CI mean
3.72 4 3 [3.51; 3.93]

Describing the spread:

var sd iqr se
1.806 1.344 2 0.1072

Describing the range:

min q1 q3 max
1 3 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.1652 -0.7391 0.1242

Describing the sample size:

total NA. valid
157 0 157

ATT1_13_ElectronicRadiation

Describing the central tendency:

mean median mode 95% CI mean
3.72 4 3 [3.51; 3.93]

Describing the spread:

var sd iqr se
1.729 1.315 2 0.1049

Describing the range:

min q1 q3 max
1 3 5 6

Describing the distribution shape:

skewness kurtosis dip
0.03481 -0.6786 0.1274

Describing the sample size:

total NA. valid
157 0 157

ATT1_14_Reincarnation

Describing the central tendency:

mean median mode 95% CI mean
3.255 3 1 [2.97; 3.53]

Describing the spread:

var sd iqr se
3.153 1.776 3 0.1417

Describing the range:

min q1 q3 max
1 1 5 6

Describing the distribution shape:

skewness kurtosis dip
0.192 -1.323 0.08599

Describing the sample size:

total NA. valid
157 0 157

ATT1_15_TrustAncientRemedies

Describing the central tendency:

mean median mode 95% CI mean
3.478 3 3 [3.27; 3.69]

Describing the spread:

var sd iqr se
1.764 1.328 1 0.106

Describing the range:

min q1 q3 max
1 2 4 6

Describing the distribution shape:

skewness kurtosis dip
-0.06782 -0.5476 0.1306

Describing the sample size:

total NA. valid
157 0 157

ATT1_16_EverythingConnected

Describing the central tendency:

mean median mode 95% CI mean
4.637 5 6 [4.43; 4.84]

Describing the spread:

var sd iqr se
1.694 1.302 2 0.1039

Describing the range:

min q1 q3 max
1 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.7644 0.05167 0.1401

Describing the sample size:

total NA. valid
157 0 157

ATT1_17_SickByChance

Describing the central tendency:

mean median mode 95% CI mean
2.255 2 1 [2.07; 2.44]

Describing the spread:

var sd iqr se
1.422 1.192 2 0.09517

Describing the range:

min q1 q3 max
1 1 3 6

Describing the distribution shape:

skewness kurtosis dip
0.8727 0.608 0.1465

Describing the sample size:

total NA. valid
157 0 157

ATT1_18_TreatsOnlySymptoms

Describing the central tendency:

mean median mode 95% CI mean
3.631 4 3 [3.39; 3.87]

Describing the spread:

var sd iqr se
2.273 1.508 2 0.1203

Describing the range:

min q1 q3 max
1 3 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.08928 -0.9218 0.1019

Describing the sample size:

total NA. valid
157 0 157

ATT1_19_DoctorMustHealMe

Describing the central tendency:

mean median mode 95% CI mean
3.268 3 (multi) [3.07; 3.47]

Describing the spread:

var sd iqr se
1.62 1.273 2 0.1016

Describing the range:

min q1 q3 max
1 2 4 6

Describing the distribution shape:

skewness kurtosis dip
-0.04364 -0.4666 0.1465

Describing the sample size:

total NA. valid
157 0 157

ATT1_20_HealingIsLuck

Describing the central tendency:

mean median mode 95% CI mean
1.962 2 1 [1.81; 2.12]

Describing the spread:

var sd iqr se
0.9472 0.9733 2 0.07768

Describing the range:

min q1 q3 max
1 1 3 5

Describing the distribution shape:

skewness kurtosis dip
0.88 0.3997 0.172

Describing the sample size:

total NA. valid
157 0 157

ATT1_21_IllnessBecauseOfEmotions

Describing the central tendency:

mean median mode 95% CI mean
3.917 4 4 [3.68; 4.15]

Describing the spread:

var sd iqr se
2.179 1.476 2 0.1178

Describing the range:

min q1 q3 max
1 2.5 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.437 -0.579 0.1115

Describing the sample size:

total NA. valid
157 0 157

ATT1_22_AvoidPharma

Describing the central tendency:

mean median mode 95% CI mean
4.344 5 5 [4.13; 4.56]

Describing the spread:

var sd iqr se
1.868 1.367 2 0.1091

Describing the range:

min q1 q3 max
1 3 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.491 -0.6179 0.1242

Describing the sample size:

total NA. valid
157 0 157

ATT1_23_NothingByChance

Describing the central tendency:

mean median mode 95% CI mean
4.446 5 (multi) [4.22; 4.67]

Describing the spread:

var sd iqr se
2.082 1.443 2 0.1152

Describing the range:

min q1 q3 max
1 3 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.8566 0.01703 0.1401

Describing the sample size:

total NA. valid
157 0 157

ATT1_24_IllnessBecauseOfGenes

Describing the central tendency:

mean median mode 95% CI mean
2.726 3 (multi) [2.55; 2.9]

Describing the spread:

var sd iqr se
1.213 1.101 1 0.0879

Describing the range:

min q1 q3 max
1 2 4 6

Describing the distribution shape:

skewness kurtosis dip
0.3874 -0.0781 0.1592

Describing the sample size:

total NA. valid
157 0 157

ATT1_25_ChemotherapyHarmful

Describing the central tendency:

mean median mode 95% CI mean
3.739 4 3 [3.51; 3.97]

Describing the spread:

var sd iqr se
2.092 1.446 2 0.1154

Describing the range:

min q1 q3 max
1 3 6 6

Describing the distribution shape:

skewness kurtosis dip
0.003063 -0.7741 0.1083

Describing the sample size:

total NA. valid
157 0 157

ATT1_26_HealingResultOfEmoDevelopment

Describing the central tendency:

mean median mode 95% CI mean
3.293 3 3 [3.08; 3.51]

Describing the spread:

var sd iqr se
1.85 1.36 2 0.1085

Describing the range:

min q1 q3 max
1 2 4 6

Describing the distribution shape:

skewness kurtosis dip
0.05794 -0.64 0.1242

Describing the sample size:

total NA. valid
157 0 157

ATT1_27_EnergeticSystemInBody

Describing the central tendency:

mean median mode 95% CI mean
4.255 5 6 [4; 4.51]

Describing the spread:

var sd iqr se
2.653 1.629 3 0.13

Describing the range:

min q1 q3 max
1 3 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.6897 -0.6408 0.121

Describing the sample size:

total NA. valid
157 0 157

ATT1_28_ConfrontingEmotionalProb

Describing the central tendency:

mean median mode 95% CI mean
3.745 4 4 [3.52; 3.97]

Describing the spread:

var sd iqr se
2.089 1.445 2 0.1153

Describing the range:

min q1 q3 max
1 3 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.2159 -0.6972 0.1146

Describing the sample size:

total NA. valid
157 0 157

ATT1_29_RadiationTherapyHarmful

Describing the central tendency:

mean median mode 95% CI mean
3.758 4 3 [3.54; 3.98]

Describing the spread:

var sd iqr se
1.98 1.407 2 0.1123

Describing the range:

min q1 q3 max
1 3 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.05024 -0.6376 0.121

Describing the sample size:

total NA. valid
157 0 157

ATT1_30_MandatoryVaccines

Describing the central tendency:

mean median mode 95% CI mean
2.968 3 1 [2.71; 3.23]

Describing the spread:

var sd iqr se
2.736 1.654 2 0.132

Describing the range:

min q1 q3 max
1 1 5 6

Describing the distribution shape:

skewness kurtosis dip
0.4301 -0.9812 0.1051

Describing the sample size:

total NA. valid
157 0 157

ATT1_31_IllnessSymbology

Describing the central tendency:

mean median mode 95% CI mean
3.567 4 (multi) [3.31; 3.82]

Describing the spread:

var sd iqr se
2.593 1.61 3 0.1285

Describing the range:

min q1 q3 max
1 2 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.1082 -1.113 0.09873

Describing the sample size:

total NA. valid
157 0 157

ATT1_32_TrustWesternDocs

Describing the central tendency:

mean median mode 95% CI mean
3.917 4 4 [3.72; 4.12]

Describing the spread:

var sd iqr se
1.576 1.256 2 0.1002

Describing the range:

min q1 q3 max
1 3 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.4127 0.07021 0.1242

Describing the sample size:

total NA. valid
157 0 157

ATT1_33_SymptomsWillDisappear

Describing the central tendency:

mean median mode 95% CI mean
3.86 4 4 [3.67; 4.05]

Describing the spread:

var sd iqr se
1.416 1.19 2 0.09497

Describing the range:

min q1 q3 max
1 3 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.3495 -0.1385 0.1146

Describing the sample size:

total NA. valid
157 0 157

ATT1_34_EnergyInEastern

Describing the central tendency:

mean median mode 95% CI mean
4.28 4 5 [4.07; 4.49]

Describing the spread:

var sd iqr se
1.703 1.305 1 0.1041

Describing the range:

min q1 q3 max
1 2 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.9198 0.5815 0.1561

Describing the sample size:

total NA. valid
157 0 157

ATT1_35_SeriousSymptom

Describing the central tendency:

mean median mode 95% CI mean
4.185 4 5 [3.95; 4.41]

Describing the spread:

var sd iqr se
2.126 1.458 2 0.1164

Describing the range:

min q1 q3 max
1 2 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.6028 -0.4739 0.121

Describing the sample size:

total NA. valid
157 0 157

ATT1_36_UnprocessedTrauma

Describing the central tendency:

mean median mode 95% CI mean
4.688 5 5 [4.51; 4.86]

Describing the spread:

var sd iqr se
1.216 1.103 2 0.08801

Describing the range:

min q1 q3 max
1 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.8067 0.6382 0.1306

Describing the sample size:

total NA. valid
157 0 157

ATT1_37_NoBiopsy

Describing the central tendency:

mean median mode 95% CI mean
2.623 3 1 [2.39; 2.86]

Describing the spread:

var sd iqr se
2.145 1.465 3 0.118

Describing the range:

min q1 q3 max
1 1 4 6

Describing the distribution shape:

skewness kurtosis dip
0.487 -0.7611 0.1266

Describing the sample size:

total NA. valid
157 3 154

ATT1_38_IllnessTeachesUs

Describing the central tendency:

mean median mode 95% CI mean
3.745 4 4 [3.51; 3.98]

Describing the spread:

var sd iqr se
2.153 1.467 2 0.1171

Describing the range:

min q1 q3 max
1 2.5 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.24 -0.8165 0.1115

Describing the sample size:

total NA. valid
157 0 157

ATT1_39_OnlyNatural

Describing the central tendency:

mean median mode 95% CI mean
3.191 3 3 [2.98; 3.4]

Describing the spread:

var sd iqr se
1.822 1.35 2 0.1077

Describing the range:

min q1 q3 max
1 2 4 6

Describing the distribution shape:

skewness kurtosis dip
0.1213 -0.6503 0.1019

Describing the sample size:

total NA. valid
157 0 157

ATT1_40_StrongerComplaintsMeanHealing

Describing the central tendency:

mean median mode 95% CI mean
3.076 3 3 [2.86; 3.29]

Describing the spread:

var sd iqr se
1.84 1.357 2 0.1083

Describing the range:

min q1 q3 max
1 2 4 6

Describing the distribution shape:

skewness kurtosis dip
0.2812 -0.5233 0.09873

Describing the sample size:

total NA. valid
157 0 157

ATT2_1_PositiveThinking

Describing the central tendency:

mean median mode 95% CI mean
4.981 5 5 [4.82; 5.14]

Describing the spread:

var sd iqr se
1.07 1.034 2 0.08256

Describing the range:

min q1 q3 max
1 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-1.439 3.107 0.172

Describing the sample size:

total NA. valid
157 0 157

ATT2_2_NeedsScientificInquiry

Describing the central tendency:

mean median mode 95% CI mean
4.14 4 5 [3.91; 4.37]

Describing the spread:

var sd iqr se
2.083 1.443 2 0.1152

Describing the range:

min q1 q3 max
1 2 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.5336 -0.5584 0.1242

Describing the sample size:

total NA. valid
157 0 157

ATT2_3_StressfulTimes

Describing the central tendency:

mean median mode 95% CI mean
5.287 5 5 [5.18; 5.4]

Describing the spread:

var sd iqr se
0.475 0.6892 1 0.05501

Describing the range:

min q1 q3 max
3 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.5636 -0.2836 0.207

Describing the sample size:

total NA. valid
157 0 157

ATT2_4_CanBeDangerous

Describing the central tendency:

mean median mode 95% CI mean
2.724 3 (multi) [2.51; 2.94]

Describing the spread:

var sd iqr se
1.904 1.38 1.5 0.1105

Describing the range:

min q1 q3 max
1 2 4 6

Describing the distribution shape:

skewness kurtosis dip
0.6425 -0.1952 0.1346

Describing the sample size:

total NA. valid
157 1 156

ATT2_5_DepressionCanWorsen

Describing the central tendency:

mean median mode 95% CI mean
5.154 5 5 [5.03; 5.28]

Describing the spread:

var sd iqr se
0.6342 0.7964 1 0.06376

Describing the range:

min q1 q3 max
1 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-1.215 3.772 0.1763

Describing the sample size:

total NA. valid
157 1 156

ATT2_6_CAMisLastResort

Describing the central tendency:

mean median mode 95% CI mean
2.353 2 2 [2.16; 2.55]

Describing the spread:

var sd iqr se
1.481 1.217 2 0.09745

Describing the range:

min q1 q3 max
1 1 3 6

Describing the distribution shape:

skewness kurtosis dip
0.8602 0.4026 0.141

Describing the sample size:

total NA. valid
157 1 156

ATT2_7_StressCausesIllness

Describing the central tendency:

mean median mode 95% CI mean
4.739 5 5 [4.59; 4.89]

Describing the spread:

var sd iqr se
0.8737 0.9347 1 0.0746

Describing the range:

min q1 q3 max
2 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.5043 0.08542 0.1465

Describing the sample size:

total NA. valid
157 0 157

ATT2_8_BeforeTurnToDoc

Describing the central tendency:

mean median mode 95% CI mean
3.477 3 3 [3.22; 3.74]

Describing the spread:

var sd iqr se
2.654 1.629 3 0.1308

Describing the range:

min q1 q3 max
0 1 5 6

Describing the distribution shape:

skewness kurtosis dip
-0.1007 -0.9967 0.1

Describing the sample size:

total NA. valid
157 2 155

ATT2_9_OnlyforLessorComplaints

Describing the central tendency:

mean median mode 95% CI mean
2.994 3 3 [2.79; 3.2]

Describing the spread:

var sd iqr se
1.64 1.281 2 0.1032

Describing the range:

min q1 q3 max
1 2 4 6

Describing the distribution shape:

skewness kurtosis dip
0.3337 -0.3882 0.1266

Describing the sample size:

total NA. valid
157 3 154

ATT2_10_BalanceBTWNworkNrest

Describing the central tendency:

mean median mode 95% CI mean
5.459 6 6 [5.35; 5.56]

Describing the spread:

var sd iqr se
0.4422 0.665 1 0.05307

Describing the range:

min q1 q3 max
3 5 NA 6

Describing the distribution shape:

skewness kurtosis dip
-0.9726 0.376 0.1847

Describing the sample size:

total NA. valid
157 0 157

ATT2_11_StrengthensOwnDefenses

Describing the central tendency:

mean median mode 95% CI mean
4.369 5 4 [4.15; 4.59]

Describing the spread:

var sd iqr se
1.978 1.406 1 0.1122

Describing the range:

min q1 q3 max
1 4 6 6

Describing the distribution shape:

skewness kurtosis dip
-0.7513 -0.09932 0.1306

Describing the sample size:

total NA. valid
157 0 157 
## Warning in max(freqs): no non-missing arguments to max; returning -Inf
## Warning in qt(res$intermediate$alpha/2, df = n - 1): NaNs produced
## Warning in qt(1 - res$intermediate$alpha/2, df = n - 1): NaNs produced

VAR00009

Quitting from lines 237-254 (network-analysis-of-alternative-medicine-attitudes.Rmd) Error in data.frame(mean = mean(x), median = median(x), mode = mode, meanCI = meanCi) : arguments imply differing number of rows: 1, 0 Calls: … get_descriptives -> paste0 -> pander -> -> data.frame

filter_$

Describing the central tendency:

mean median mode 95% CI mean
0.3949 0 0 [0.32; 0.47]

Describing the spread:

var sd iqr se
0.2405 0.4904 1 0.03914

Describing the range:

min q1 q3 max
0 NA 1 1

Describing the distribution shape:

skewness kurtosis dip
0.4341 -1.835 0.1975

Describing the sample size:

total NA. valid
157 0 157

group_tri

  Frequencies Perc.Total Perc.Valid Cumulative
Biomed 62 39.5 39.5 39.5
Alternative 45 28.7 28.7 68.2
Complementary 50 31.8 31.8 100
Total valid 157 100 100

Correlations

All attitude variables together

### Takes way too long, huge, etc
# GGally::ggpairs(dat[, attitudeVars]);

cors <- cor(dat[, attitudeVars],
            use='complete.obs');

knitr::kable(cors);
ATT1_1_ExerciseAndDiet ATT1_2_MustSuffer ATT1_3_TrustInTradRemedy ATT1_4_BodyMirrorToSoul ATT1_5_DependsOnEnvironment ATT1_6_IllnessIsImbalance ATT1_7_HealthyDiet ATT1_8_NeedTestResult ATT1_9_MindHasStrongEffect ATT1_10_AttractPplAndEvents ATT1_11_BodyRemembers ATT1_12_WeakImmuneSystem ATT1_13_ElectronicRadiation ATT1_14_Reincarnation ATT1_15_TrustAncientRemedies ATT1_16_EverythingConnected ATT1_17_SickByChance ATT1_18_TreatsOnlySymptoms ATT1_19_DoctorMustHealMe ATT1_20_HealingIsLuck ATT1_21_IllnessBecauseOfEmotions ATT1_22_AvoidPharma ATT1_23_NothingByChance ATT1_24_IllnessBecauseOfGenes ATT1_25_ChemotherapyHarmful ATT1_26_HealingResultOfEmoDevelopment ATT1_27_EnergeticSystemInBody ATT1_28_ConfrontingEmotionalProb ATT1_29_RadiationTherapyHarmful ATT1_30_MandatoryVaccines ATT1_31_IllnessSymbology ATT1_32_TrustWesternDocs ATT1_33_SymptomsWillDisappear ATT1_34_EnergyInEastern ATT1_35_SeriousSymptom ATT1_36_UnprocessedTrauma ATT1_37_NoBiopsy ATT1_38_IllnessTeachesUs ATT1_39_OnlyNatural ATT1_40_StrongerComplaintsMeanHealing
ATT1_1_ExerciseAndDiet 1.0000000 0.0054161 0.0687178 0.4206610 -0.0414439 0.2658708 0.2202788 -0.0967743 0.2122778 0.0760936 0.1892129 0.1034782 0.0197600 0.0730772 0.1222786 0.1458379 0.0062962 0.1090275 0.0362593 -0.1254873 0.2249561 0.1121921 0.1596211 -0.0164830 0.1906475 0.2045420 0.1596584 0.2316194 0.1872051 -0.0340306 0.1517998 -0.0290251 0.1898578 0.0832831 -0.1314154 0.0743992 0.0377949 0.1966049 0.1688695 0.1461166
ATT1_2_MustSuffer 0.0054161 1.0000000 0.0399843 0.1360426 0.3293624 0.0756668 -0.0443301 0.0252070 0.0776545 0.0814977 0.0262516 0.1212355 0.0694189 -0.0522676 -0.0062933 0.0418795 0.1082750 -0.0678401 0.0841814 -0.0273832 -0.0240271 -0.0610453 0.0322213 0.0786162 0.1472411 0.0347830 -0.0862025 -0.0507096 0.1495866 0.0462063 -0.0716466 -0.0212468 -0.0415685 -0.0987391 0.0115117 0.0925523 -0.0252680 -0.0220949 -0.0304308 -0.0737804
ATT1_3_TrustInTradRemedy 0.0687178 0.0399843 1.0000000 0.2249171 -0.0482545 0.2019742 0.0566226 -0.3493843 0.3472783 0.2674716 0.3485551 -0.0169306 0.1413999 0.3188806 0.4925796 0.2322353 -0.2312852 0.3851676 0.0061853 -0.2851330 0.2227620 0.3985563 0.3275579 -0.2124415 0.3874647 0.2926369 0.3538216 0.2801188 0.4184651 0.2686508 0.3136477 -0.5087061 0.1984842 0.2455830 -0.5663668 0.2266169 0.2334781 0.3287663 0.4951061 0.3783619
ATT1_4_BodyMirrorToSoul 0.4206610 0.1360426 0.2249171 1.0000000 0.2644718 0.4779496 0.2469704 -0.1773445 0.4429715 0.4876800 0.5360377 0.1440485 0.2358389 0.2801344 0.2823573 0.4375893 -0.0647945 0.2574385 -0.0264387 -0.1218190 0.4357582 0.2865663 0.4246552 -0.0219990 0.2648194 0.4122420 0.3586105 0.4398906 0.3409473 0.1974204 0.3989796 -0.1081250 0.4612814 0.3063318 -0.2116695 0.4052624 0.1882564 0.3545914 0.2240950 0.2303519
ATT1_5_DependsOnEnvironment -0.0414439 0.3293624 -0.0482545 0.2644718 1.0000000 0.1200548 -0.1975226 0.0830335 0.2045356 0.1821657 0.0094967 0.1873449 -0.0446195 -0.0713644 -0.0171854 0.0381642 0.2457939 -0.1112211 0.2337019 0.2034428 0.1415459 -0.1093726 0.0530034 0.2104045 -0.0806387 0.1341475 0.0425695 0.0881977 0.0095487 0.1065393 0.0068710 0.0294318 0.1044473 0.0123156 -0.0079130 0.0407223 0.1606185 0.0722145 -0.0351267 -0.0293130
ATT1_6_IllnessIsImbalance 0.2658708 0.0756668 0.2019742 0.4779496 0.1200548 1.0000000 0.2251774 -0.1567106 0.4942181 0.3714301 0.5247270 0.2674479 0.2621854 0.2540184 0.3298663 0.4027674 -0.1979819 0.3487299 -0.0609286 -0.1203476 0.5529565 0.3709777 0.4331098 0.0769675 0.3507904 0.4152025 0.4152410 0.4767843 0.3305618 0.2681004 0.5140983 -0.2432006 0.4812476 0.3079574 -0.2762678 0.5847060 0.1759754 0.4818995 0.2912395 0.2816260
ATT1_7_HealthyDiet 0.2202788 -0.0443301 0.0566226 0.2469704 -0.1975226 0.2251774 1.0000000 -0.0577381 0.1661579 0.0937240 0.2816853 0.0937958 0.2443342 0.1965104 0.0586149 0.0950937 -0.1742249 0.1239174 -0.2131927 -0.0270183 0.1236547 0.2317797 0.1740472 -0.1791752 0.1441873 0.1831216 0.2162358 0.1749697 0.1290462 0.0485263 0.2555365 -0.0503647 0.1477357 0.0778997 -0.1119189 0.2489982 0.0698320 0.2527524 0.1982211 0.1957129
ATT1_8_NeedTestResult -0.0967743 0.0252070 -0.3493843 -0.1773445 0.0830335 -0.1567106 -0.0577381 1.0000000 -0.0520796 -0.1485616 -0.1486377 0.0247272 -0.0167402 -0.1849931 -0.2613940 -0.1658542 0.0969698 -0.2789160 0.2018276 0.1247677 -0.1018884 -0.2362467 -0.2229017 0.1715445 -0.2803159 -0.1360690 -0.2433245 -0.2243612 -0.2923480 -0.1034996 -0.2434594 0.3172283 -0.1670566 -0.1592532 0.4402867 -0.0444246 -0.0619809 -0.1714422 -0.2775466 -0.1582221
ATT1_9_MindHasStrongEffect 0.2122778 0.0776545 0.3472783 0.4429715 0.2045356 0.4942181 0.1661579 -0.0520796 1.0000000 0.5022850 0.5206856 0.1789726 0.2818216 0.4038429 0.3913952 0.4459452 -0.2293779 0.3244870 -0.0896558 -0.0758399 0.5388819 0.3928680 0.5244709 0.0436545 0.3016080 0.4885815 0.4698365 0.5385189 0.3117382 0.2482769 0.4417475 -0.2520904 0.4397829 0.3413725 -0.3279419 0.4854531 0.2312013 0.4841892 0.2979948 0.3670536
ATT1_10_AttractPplAndEvents 0.0760936 0.0814977 0.2674716 0.4876800 0.1821657 0.3714301 0.0937240 -0.1485616 0.5022850 1.0000000 0.5235645 0.0993749 0.3326897 0.4626649 0.3811754 0.6130047 -0.2588225 0.3476191 -0.0997466 -0.1291992 0.4452941 0.2820609 0.5811317 -0.1367010 0.2825817 0.4091527 0.5430469 0.4736931 0.3240820 0.1639470 0.5365687 -0.2417653 0.3960050 0.4959991 -0.2776490 0.4367480 0.1414104 0.4262265 0.2030081 0.2852613
ATT1_11_BodyRemembers 0.1892129 0.0262516 0.3485551 0.5360377 0.0094967 0.5247270 0.2816853 -0.1486377 0.5206856 0.5235645 1.0000000 0.1312519 0.4138740 0.4764432 0.4066875 0.5147970 -0.2925920 0.3803597 -0.1579940 -0.1256117 0.4898779 0.4032909 0.5287270 -0.0610542 0.3346039 0.4876131 0.6052014 0.5692964 0.3485249 0.3349171 0.6052088 -0.1897516 0.5295881 0.4972222 -0.3124592 0.5754526 0.2825870 0.5686156 0.3657404 0.3253450
ATT1_12_WeakImmuneSystem 0.1034782 0.1212355 -0.0169306 0.1440485 0.1873449 0.2674479 0.0937958 0.0247272 0.1789726 0.0993749 0.1312519 1.0000000 0.2752603 0.0479077 0.0835712 0.0297011 -0.0034640 -0.0055184 0.2857657 -0.0617796 0.3200732 0.1321390 0.0471601 0.3381754 0.1719405 0.1731542 0.0834863 0.2514190 0.2075617 0.0926864 0.1847677 0.1109876 0.2273381 -0.0116188 -0.0983390 0.1690157 -0.0084918 0.2326579 0.0926467 0.0946424
ATT1_13_ElectronicRadiation 0.0197600 0.0694189 0.1413999 0.2358389 -0.0446195 0.2621854 0.2443342 -0.0167402 0.2818216 0.3326897 0.4138740 0.2752603 1.0000000 0.3555378 0.3075953 0.3902444 -0.0504260 0.1899664 -0.1472693 0.0383195 0.3629332 0.3639161 0.3729933 0.0443294 0.2510667 0.3453951 0.4484465 0.4734375 0.2937201 0.2040601 0.4658634 -0.1298798 0.2325318 0.3497912 -0.2163133 0.4491260 0.1815445 0.4567256 0.2382258 0.2388390
ATT1_14_Reincarnation 0.0730772 -0.0522676 0.3188806 0.2801344 -0.0713644 0.2540184 0.1965104 -0.1849931 0.4038429 0.4626649 0.4764432 0.0479077 0.3555378 1.0000000 0.4727992 0.4949819 -0.3822183 0.3720714 -0.2446042 -0.0097380 0.3700497 0.4332687 0.5763142 -0.1813892 0.3351660 0.4559591 0.6405835 0.5220288 0.3426169 0.1475878 0.5489846 -0.2995215 0.3574128 0.4116090 -0.3735119 0.4033991 0.2505121 0.5153787 0.4316867 0.4462609
ATT1_15_TrustAncientRemedies 0.1222786 -0.0062933 0.4925796 0.2823573 -0.0171854 0.3298663 0.0586149 -0.2613940 0.3913952 0.3811754 0.4066875 0.0835712 0.3075953 0.4727992 1.0000000 0.3855192 -0.1294319 0.5828173 0.0266763 -0.0932796 0.3212942 0.5331970 0.4771336 -0.0234370 0.4961676 0.4796036 0.5500308 0.5508476 0.5334350 0.1872026 0.4559673 -0.5413175 0.3699070 0.4266635 -0.5217664 0.3437206 0.4094316 0.5057373 0.5679982 0.4669802
ATT1_16_EverythingConnected 0.1458379 0.0418795 0.2322353 0.4375893 0.0381642 0.4027674 0.0950937 -0.1658542 0.4459452 0.6130047 0.5147970 0.0297011 0.3902444 0.4949819 0.3855192 1.0000000 -0.2424026 0.3139141 -0.2041912 -0.1176678 0.4198273 0.3257879 0.6619870 -0.1473315 0.2552401 0.3279818 0.5174101 0.4652473 0.2701384 0.2323531 0.5316003 -0.1862762 0.4002202 0.4271042 -0.2537242 0.4837864 0.1709845 0.4959438 0.2240387 0.2465442
ATT1_17_SickByChance 0.0062962 0.1082750 -0.2312852 -0.0647945 0.2457939 -0.1979819 -0.1742249 0.0969698 -0.2293779 -0.2588225 -0.2925920 -0.0034640 -0.0504260 -0.3822183 -0.1294319 -0.2424026 1.0000000 -0.0547868 0.2497055 0.2709308 -0.1708978 -0.1182007 -0.2521696 0.2942955 -0.0583775 -0.0383939 -0.1873298 -0.0814155 -0.0474106 -0.2829891 -0.3211128 0.2435520 -0.1002422 -0.0726241 0.1893255 -0.2876047 -0.0235375 -0.1934261 -0.1167337 -0.0729482
ATT1_18_TreatsOnlySymptoms 0.1090275 -0.0678401 0.3851676 0.2574385 -0.1112211 0.3487299 0.1239174 -0.2789160 0.3244870 0.3476191 0.3803597 -0.0055184 0.1899664 0.3720714 0.5828173 0.3139141 -0.0547868 1.0000000 -0.0509052 -0.0661100 0.3849268 0.4861793 0.4526995 -0.0914244 0.5365594 0.4514097 0.5846754 0.4430940 0.4434167 0.1410178 0.4160762 -0.5366664 0.4022170 0.3760035 -0.4209134 0.2675946 0.3751284 0.4160301 0.4353256 0.3681333
ATT1_19_DoctorMustHealMe 0.0362593 0.0841814 0.0061853 -0.0264387 0.2337019 -0.0609286 -0.2131927 0.2018276 -0.0896558 -0.0997466 -0.1579940 0.2857657 -0.1472693 -0.2446042 0.0266763 -0.2041912 0.2497055 -0.0509052 1.0000000 0.1102324 -0.0046624 -0.0665886 -0.2321891 0.3033737 0.0750817 0.0334014 -0.1645822 -0.0908550 0.1426962 -0.1049117 -0.1679975 0.0919329 -0.0536017 -0.1017156 0.0349323 -0.1275742 0.0517993 -0.0990084 0.0563547 -0.1921637
ATT1_20_HealingIsLuck -0.1254873 -0.0273832 -0.2851330 -0.1218190 0.2034428 -0.1203476 -0.0270183 0.1247677 -0.0758399 -0.1291992 -0.1256117 -0.0617796 0.0383195 -0.0097380 -0.0932796 -0.1176678 0.2709308 -0.0661100 0.1102324 1.0000000 -0.0613623 -0.1051493 0.0239701 0.1910189 -0.1271732 0.1051900 0.0429204 -0.0055617 -0.1255844 -0.1137934 -0.0714842 0.1369722 0.0012417 -0.0746562 0.2071352 -0.0280356 0.2436418 0.0034805 -0.0795954 -0.0577502
ATT1_21_IllnessBecauseOfEmotions 0.2249561 -0.0240271 0.2227620 0.4357582 0.1415459 0.5529565 0.1236547 -0.1018884 0.5388819 0.4452941 0.4898779 0.3200732 0.3629332 0.3700497 0.3212942 0.4198273 -0.1708978 0.3849268 -0.0046624 -0.0613623 1.0000000 0.3456913 0.4929943 0.1545845 0.3394108 0.6272069 0.5051314 0.6357904 0.3311708 0.2190891 0.6680852 -0.1926746 0.6436437 0.4242667 -0.1779632 0.5839249 0.2156674 0.5067599 0.2591468 0.3053829
ATT1_22_AvoidPharma 0.1121921 -0.0610453 0.3985563 0.2865663 -0.1093726 0.3709777 0.2317797 -0.2362467 0.3928680 0.2820609 0.4032909 0.1321390 0.3639161 0.4332687 0.5331970 0.3257879 -0.1182007 0.4861793 -0.0665886 -0.1051493 0.3456913 1.0000000 0.4738300 -0.0700838 0.4079969 0.4069011 0.4521505 0.4431287 0.4472165 0.2537590 0.4735388 -0.4527800 0.3351502 0.2885399 -0.5089255 0.4088550 0.2789144 0.4196286 0.5995711 0.4623255
ATT1_23_NothingByChance 0.1596211 0.0322213 0.3275579 0.4246552 0.0530034 0.4331098 0.1740472 -0.2229017 0.5244709 0.5811317 0.5287270 0.0471601 0.3729933 0.5763142 0.4771336 0.6619870 -0.2521696 0.4526995 -0.2321891 0.0239701 0.4929943 0.4738300 1.0000000 0.0109790 0.3681791 0.5088340 0.5826668 0.5841888 0.4133183 0.1754654 0.6537926 -0.2934296 0.4506522 0.3769215 -0.3170201 0.4871373 0.2807297 0.6780911 0.3021821 0.4063435
ATT1_24_IllnessBecauseOfGenes -0.0164830 0.0786162 -0.2124415 -0.0219990 0.2104045 0.0769675 -0.1791752 0.1715445 0.0436545 -0.1367010 -0.0610542 0.3381754 0.0443294 -0.1813892 -0.0234370 -0.1473315 0.2942955 -0.0914244 0.3033737 0.1910189 0.1545845 -0.0700838 0.0109790 1.0000000 -0.0270229 0.2135496 -0.1242561 0.0472935 -0.0074568 0.0309905 -0.0371198 0.1354120 0.0457838 -0.1547928 0.1830111 0.0267491 0.0755117 -0.0103802 -0.1452718 -0.1160408
ATT1_25_ChemotherapyHarmful 0.1906475 0.1472411 0.3874647 0.2648194 -0.0806387 0.3507904 0.1441873 -0.2803159 0.3016080 0.2825817 0.3346039 0.1719405 0.2510667 0.3351660 0.4961676 0.2552401 -0.0583775 0.5365594 0.0750817 -0.1271732 0.3394108 0.4079969 0.3681791 -0.0270229 1.0000000 0.4546396 0.4015689 0.4160576 0.8428863 0.1613083 0.3700511 -0.3874451 0.3426574 0.1531717 -0.3829592 0.3390217 0.2844477 0.3859409 0.4599611 0.3060069
ATT1_26_HealingResultOfEmoDevelopment 0.2045420 0.0347830 0.2926369 0.4122420 0.1341475 0.4152025 0.1831216 -0.1360690 0.4885815 0.4091527 0.4876131 0.1731542 0.3453951 0.4559591 0.4796036 0.3279818 -0.0383939 0.4514097 0.0334014 0.1051900 0.6272069 0.4069011 0.5088340 0.2135496 0.4546396 1.0000000 0.5238756 0.6860565 0.4238871 0.2454408 0.6083206 -0.2645614 0.6146357 0.4278928 -0.3004552 0.4424484 0.3652104 0.6161122 0.3447994 0.3893012
ATT1_27_EnergeticSystemInBody 0.1596584 -0.0862025 0.3538216 0.3586105 0.0425695 0.4152410 0.2162358 -0.2433245 0.4698365 0.5430469 0.6052014 0.0834863 0.4484465 0.6405835 0.5500308 0.5174101 -0.1873298 0.5846754 -0.1645822 0.0429204 0.5051314 0.4521505 0.5826668 -0.1242561 0.4015689 0.5238756 1.0000000 0.7000795 0.3820224 0.2271138 0.6132902 -0.3120822 0.5348839 0.6521425 -0.4546381 0.5079048 0.4149599 0.6549123 0.4553830 0.4556764
ATT1_28_ConfrontingEmotionalProb 0.2316194 -0.0507096 0.2801188 0.4398906 0.0881977 0.4767843 0.1749697 -0.2243612 0.5385189 0.4736931 0.5692964 0.2514190 0.4734375 0.5220288 0.5508476 0.4652473 -0.0814155 0.4430940 -0.0908550 -0.0055617 0.6357904 0.4431287 0.5841888 0.0472935 0.4160576 0.6860565 0.7000795 1.0000000 0.4046572 0.2143869 0.6510185 -0.2109775 0.7099183 0.5365014 -0.3740117 0.5366858 0.3231583 0.6910299 0.3852736 0.4165323
ATT1_29_RadiationTherapyHarmful 0.1872051 0.1495866 0.4184651 0.3409473 0.0095487 0.3305618 0.1290462 -0.2923480 0.3117382 0.3240820 0.3485249 0.2075617 0.2937201 0.3426169 0.5334350 0.2701384 -0.0474106 0.4434167 0.1426962 -0.1255844 0.3311708 0.4472165 0.4133183 -0.0074568 0.8428863 0.4238871 0.3820224 0.4046572 1.0000000 0.1547363 0.3815457 -0.3602146 0.3113579 0.1294614 -0.4278274 0.3487336 0.2931028 0.4152680 0.5058298 0.3417558
ATT1_30_MandatoryVaccines -0.0340306 0.0462063 0.2686508 0.1974204 0.1065393 0.2681004 0.0485263 -0.1034996 0.2482769 0.1639470 0.3349171 0.0926864 0.2040601 0.1475878 0.1872026 0.2323531 -0.2829891 0.1410178 -0.1049117 -0.1137934 0.2190891 0.2537590 0.1754654 0.0309905 0.1613083 0.2454408 0.2271138 0.2143869 0.1547363 1.0000000 0.1954700 -0.3064897 0.1714389 0.0793773 -0.2846922 0.2840609 0.3333933 0.2076523 0.2054123 0.1289035
ATT1_31_IllnessSymbology 0.1517998 -0.0716466 0.3136477 0.3989796 0.0068710 0.5140983 0.2555365 -0.2434594 0.4417475 0.5365687 0.6052088 0.1847677 0.4658634 0.5489846 0.4559673 0.5316003 -0.3211128 0.4160762 -0.1679975 -0.0714842 0.6680852 0.4735388 0.6537926 -0.0371198 0.3700511 0.6083206 0.6132902 0.6510185 0.3815457 0.1954700 1.0000000 -0.2869571 0.5645111 0.4660545 -0.3801909 0.5500127 0.1790779 0.6952910 0.4007775 0.4305977
ATT1_32_TrustWesternDocs -0.0290251 -0.0212468 -0.5087061 -0.1081250 0.0294318 -0.2432006 -0.0503647 0.3172283 -0.2520904 -0.2417653 -0.1897516 0.1109876 -0.1298798 -0.2995215 -0.5413175 -0.1862762 0.2435520 -0.5366664 0.0919329 0.1369722 -0.1926746 -0.4527800 -0.2934296 0.1354120 -0.3874451 -0.2645614 -0.3120822 -0.2109775 -0.3602146 -0.3064897 -0.2869571 1.0000000 -0.0889459 -0.1661708 0.5801770 -0.1927122 -0.3233230 -0.2108713 -0.4836589 -0.1966027
ATT1_33_SymptomsWillDisappear 0.1898578 -0.0415685 0.1984842 0.4612814 0.1044473 0.4812476 0.1477357 -0.1670566 0.4397829 0.3960050 0.5295881 0.2273381 0.2325318 0.3574128 0.3699070 0.4002202 -0.1002422 0.4022170 -0.0536017 0.0012417 0.6436437 0.3351502 0.4506522 0.0457838 0.3426574 0.6146357 0.5348839 0.7099183 0.3113579 0.1714389 0.5645111 -0.0889459 1.0000000 0.4559997 -0.2067542 0.5295790 0.2502046 0.6034346 0.3033756 0.3195752
ATT1_34_EnergyInEastern 0.0832831 -0.0987391 0.2455830 0.3063318 0.0123156 0.3079574 0.0778997 -0.1592532 0.3413725 0.4959991 0.4972222 -0.0116188 0.3497912 0.4116090 0.4266635 0.4271042 -0.0726241 0.3760035 -0.1017156 -0.0746562 0.4242667 0.2885399 0.3769215 -0.1547928 0.1531717 0.4278928 0.6521425 0.5365014 0.1294614 0.0793773 0.4660545 -0.1661708 0.4559997 1.0000000 -0.2287806 0.3951118 0.1294561 0.4327702 0.2773837 0.2903717
ATT1_35_SeriousSymptom -0.1314154 0.0115117 -0.5663668 -0.2116695 -0.0079130 -0.2762678 -0.1119189 0.4402867 -0.3279419 -0.2776490 -0.3124592 -0.0983390 -0.2163133 -0.3735119 -0.5217664 -0.2537242 0.1893255 -0.4209134 0.0349323 0.2071352 -0.1779632 -0.5089255 -0.3170201 0.1830111 -0.3829592 -0.3004552 -0.4546381 -0.3740117 -0.4278274 -0.2846922 -0.3801909 0.5801770 -0.2067542 -0.2287806 1.0000000 -0.1848200 -0.2706797 -0.3907500 -0.6067400 -0.4759387
ATT1_36_UnprocessedTrauma 0.0743992 0.0925523 0.2266169 0.4052624 0.0407223 0.5847060 0.2489982 -0.0444246 0.4854531 0.4367480 0.5754526 0.1690157 0.4491260 0.4033991 0.3437206 0.4837864 -0.2876047 0.2675946 -0.1275742 -0.0280356 0.5839249 0.4088550 0.4871373 0.0267491 0.3390217 0.4424484 0.5079048 0.5366858 0.3487336 0.2840609 0.5500127 -0.1927122 0.5295790 0.3951118 -0.1848200 1.0000000 0.1900231 0.5531866 0.3412182 0.3970854
ATT1_37_NoBiopsy 0.0377949 -0.0252680 0.2334781 0.1882564 0.1606185 0.1759754 0.0698320 -0.0619809 0.2312013 0.1414104 0.2825870 -0.0084918 0.1815445 0.2505121 0.4094316 0.1709845 -0.0235375 0.3751284 0.0517993 0.2436418 0.2156674 0.2789144 0.2807297 0.0755117 0.2844477 0.3652104 0.4149599 0.3231583 0.2931028 0.3333933 0.1790779 -0.3233230 0.2502046 0.1294561 -0.2706797 0.1900231 1.0000000 0.4094720 0.2964953 0.1801603
ATT1_38_IllnessTeachesUs 0.1966049 -0.0220949 0.3287663 0.3545914 0.0722145 0.4818995 0.2527524 -0.1714422 0.4841892 0.4262265 0.5686156 0.2326579 0.4567256 0.5153787 0.5057373 0.4959438 -0.1934261 0.4160301 -0.0990084 0.0034805 0.5067599 0.4196286 0.6780911 -0.0103802 0.3859409 0.6161122 0.6549123 0.6910299 0.4152680 0.2076523 0.6952910 -0.2108713 0.6034346 0.4327702 -0.3907500 0.5531866 0.4094720 1.0000000 0.3786605 0.5594566
ATT1_39_OnlyNatural 0.1688695 -0.0304308 0.4951061 0.2240950 -0.0351267 0.2912395 0.1982211 -0.2775466 0.2979948 0.2030081 0.3657404 0.0926467 0.2382258 0.4316867 0.5679982 0.2240387 -0.1167337 0.4353256 0.0563547 -0.0795954 0.2591468 0.5995711 0.3021821 -0.1452718 0.4599611 0.3447994 0.4553830 0.3852736 0.5058298 0.2054123 0.4007775 -0.4836589 0.3033756 0.2773837 -0.6067400 0.3412182 0.2964953 0.3786605 1.0000000 0.5112160
ATT1_40_StrongerComplaintsMeanHealing 0.1461166 -0.0737804 0.3783619 0.2303519 -0.0293130 0.2816260 0.1957129 -0.1582221 0.3670536 0.2852613 0.3253450 0.0946424 0.2388390 0.4462609 0.4669802 0.2465442 -0.0729482 0.3681333 -0.1921637 -0.0577502 0.3053829 0.4623255 0.4063435 -0.1160408 0.3060069 0.3893012 0.4556764 0.4165323 0.3417558 0.1289035 0.4305977 -0.1966027 0.3195752 0.2903717 -0.4759387 0.3970854 0.1801603 0.5594566 0.5112160 1.0000000 
### Save to csv
write.csv(cors,
          file=file.path(workingPath,
                         "correlations--bivariate--all-cases.csv"));

### http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2
                
ggcorrplot::ggcorrplot(cors);

ggcorrplot::ggcorrplot(cors,
                       method = "circle");

ggcorrplot(cors,
           hc.order = TRUE,
           outline.col = "white")

ggcorrplot::ggcorrplot(cors,
                       lab = TRUE);

Pairwise scatterplots

for (xAxisVar in attitudeVars) {

  ufs::cat0("\n\n#### ", xAxisVar,
            " {.tabset .tabset-fade .tabset-pills}\n\n");

  for (yAxisVar in tail(attitudeVars, -1)) {

    ### Only for 'half the matrix'
    if (which(attitudeVars == yAxisVar) >
        which(attitudeVars == xAxisVar)) {
      ufs::cat0("\n\n##### ", yAxisVar,
                "\n\n");
      print(ggplot2::ggplot(data=dat,
                            mapping=ggplot2::aes_string(x=xAxisVar,
                                                        y=yAxisVar,
                                                        color='group_tri')) +
              geom_jitter(size=3) +
              theme_minimal());
    }
  }
}

[NOTE: PROBABLY OBSOLETE] Network analyses

For the network analysis section we will first estimate a full network where a choice of medical practice is regarded as a system component. This will allow us to explore how attitudes relate to the behavior of interest and the relative importance of each determinant. Afterwards, we will create sub samples based on a variable that represents groups of people that prefere one or other medical practice. This way we can investigate structural features of networks (topologies) that are peculiar to these groups.

# here I subset data that is going to be used in the network analysis with the behavior as a component.

subset <- dat[, c(attitudeVars, 
                 "group_tri")];

# I eliminated other variables of interest (sex, age etc.) because the N in subgroups are very small and the number of "predictor" variables would exceed the number of datapoints.  


### Here I create subsets based on the grouping variable.
for(i in levels(subset$group_tri)){
  assign(paste("subset",
               i,
               sep = "_"),
         subset(subset,
                subset$group_tri == i))    
}

subset$group_tri <-
  as.numeric(subset$group_tri);

subset <-
  subset %>% tidyr::drop_na();

Step 1: Estimating the netork with full data.

network <-
  estimateNetwork(subset,
                  default = "EBICglasso");

plot(network,
     layout = 'spring',
     labels = colnames(network),
     title = c('Figure 1: A Network with the behavioral variable'));

### Also store to disk - Sam, you can copy this for any other plots of course
pdf(here::here('results-intermediate',
               'network-with-grouping-variable.pdf'));
plot(network,
     layout = 'spring',
     labels = colnames(network),
     title = c('Figure 1: A Network with the behavioral variable'));
dev.off();

# Calculating centrality measures.
pdf(here::here('results-intermediate',
               'centrality_plot.pdf'));
centralityPlot(network,
               include = "all");
dev.off();

# # Checking the stability of the centrality measures
# central_stability <- 
#   bootnet(network,
#           nCores = 20,
#           nBoots = 1000,
#           type = 'case');
# 
# pdf(here::here('results-intermediate',
#                'centrality_stability_plot.pdf'));
# plot(central_stability)
# dev.off();
# 
# # Checking the stability/reliability of the edge weights
# edgewgt <-
#   bootnet(network,
#           nCores = 20,
#           nBoots = 2500);
# 
# plot(edgewgt,
#      labels = FALSE,
#      order = 'sample');
# 
# pdf(here::here('results-intermediate',
#                'edge_weights.pdf'));
# plot(edgewgt,
#      labels = FALSE,
#      order = 'sample');
# dev.off();

Exploring shortest paths from each node to the behavior of interest.

Before applying Dijkstra’s algorithm we need to 1/the corelation matrix to invert it so the strongest connections will be represented with smaller numbers and the smallest correlations with larger.

### First we need to take the absoulte values of the adjacency matrix and devide 1 by the matrix. Then recreate a network object for further analysis.

absolute_adj <-
  abs(network$graph);

for (i in which(absolute_adj > 0)) {
  absolute_adj[i] = 1/absolute_adj[i] 
}

graph_full <- 
  graph.adjacency(absolute_adj,
                  mode = 'undirected',
                  weighted = TRUE);

# Calculate shortest path to the outcome variable and then delete the last row of the dataframe that includes the outcome variable (shortest path to itself = 0) 

dijkstra_fullnetwork <-
  igraph::distances(graph_full,
                    v = V(graph_full),
                    to = 41,
                    algorithm = "dijkstra");

dijkstra_ful <-
  subset(dijkstra_fullnetwork,
         dijkstra_fullnetwork[,] == min(dijkstra_fullnetwork[-c(41),]));

Exploring network structure.

Here we apply greedy hirarchical clustering algorithm to detect clusters in the data. Then we plot the results and the respective dendrogram.

fg <- 
  fastgreedy.community(graph_full, weights = E(graph_full)$weight)

fg$names <- 
  strtrim(fg$names, 7)

V(graph_full)$name <-
  strtrim(fg$names, 7)

length(fg)
sizes(fg)


set.seed(100)

par(mfrow=c(1,2))

plot(fg, graph_full, 
     vertex.label.cex=c(0.5,0.5,0.5),
     vertex.label.font=c(2))

dendPlot(fg, mode = 'phylo')

Step 2: Group Comparisons.

alternative <-
  tidyr::drop_na(subset_Alternative[,-c(41)])
biomedical <-
  tidyr::drop_na(subset_Biomed[,-c(41)])

network_fgl <-
  EstimateGroupNetwork(list('Alternative' = alternative,
                            "Biomed" = biomedical)) # We get an empty network.

Item Clustering Analysis

absolute_adj_hc <-
  abs(network$graph);


graph_full_hc <- 
  graph.adjacency(absolute_adj[-c(41),-c(41)],
                  mode = 'undirected',
                  weighted = TRUE)


fg_item <- 
  fastgreedy.community(graph_full_hc,
                       weights = E(graph_full_hc)$weight,
                       cut_a)

fg_item <-
  cut_at(fg_item,
         no=10)

fg_item$names <- 
  strtrim(fg_item$names, 7)

V(graph_full_hc)$name <-
  strtrim(fg_item$names, 7)

length(fg_item)
sizes(fg_item)


set.seed(101)

par(mfrow=c(1,2))

plot(fg_item, graph_full_hc, 
     vertex.label.cex=c(0.5,0.5,0.5),
     vertex.label.font=c(2))

dendPlot(fg_item, mode = 'phylo')


###
###     attitudeVars
###


lapply(1:20,
       function(x)
         return(attitudeVars[which(cut_at(fg_item,
                                          no=20)==x)]));

### Trying to cut the inductive dendrogram
walk <- g %>%
  cluster_walktrap() %>%
  cut_at(no = 10)

eb <- g %>%
  cluster_edge_betweenness() %>%
  cut_at(no = 10)